Advancements in mapping areas suitable for wetland habitats across the conterminous United States.

Ecosystem services (and/or nature-based solutions) Machine learning Predictive modeling Restoration (and/or conservation) Training data

Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
29 Jul 2024
Historique:
received: 27 03 2024
revised: 23 07 2024
accepted: 24 07 2024
medline: 1 8 2024
pubmed: 1 8 2024
entrez: 31 7 2024
Statut: aheadofprint

Résumé

Wetland habitats provide critical ecosystem services to the surrounding landscape, including nutrient and pollutant retention, flood mitigation, and carbon storage. Wetland connectivity to water bodies and related ecosystems is critical in habitat sustainability, but there are limited resources for landscape-level wetland planning. Considering the network connectivity of an ecosystem type can derive different benefits to the natural and built environment, as well as human health. The value that wetlands provide, along with incentive programs and conservation goals mandated by the government require new and improved wetland spatial data. Utilizing high quality, publicly available data, this study finds that the amount of land in the United States that could support built or restored wetlands is more than double the area of mapped existing wetlands. This study uses 17 input variables (i.e., features extracted from remotely sensed data and auxiliary datasets) at the 10-m resolution and the National Wetlands Inventory to train a random forest model to identify areas that may support a wetland habitat, or potential wetland areas. Models were calculated for each of 18 two-digit hydrologic units that encompass the conterminous United States, and model overall accuracy ranged from 78.0 % to 89.8 %. The models predicted that 21.1 % of the conterminous United States can be categorized as potential wetland area. Selecting input variables to predict areas with wetland potential, rather than to identify existing wetlands, using the random forest algorithm can be transferred to other locations, scales, and ecosystem types. Visualizing potential wetland areas using input data at the 10-m resolution and enhanced methodology improves previous work, as even slight changes in topography, soils, and landscape features can determine ecosystem connections. This product can be used to better place wetland restoration projects to serve ecosystem- and community-wide health by ensuring ecosystem success and targeting areas that face increased climate change impacts.

Identifiants

pubmed: 39084381
pii: S0048-9697(24)05208-2
doi: 10.1016/j.scitotenv.2024.175058
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

175058

Informations de copyright

Copyright © 2024. Published by Elsevier B.V.

Auteurs

Lauren Krohmer (L)

Oak Ridge Associated Universities supporting U.S. Environmental Protection Agency (EPA), Center for Public Health and Environmental Assessment (CPHEA), 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.

Elijah Heetderks (E)

Oak Ridge Associated Universities supporting U.S. Environmental Protection Agency (EPA), Center for Public Health and Environmental Assessment (CPHEA), 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.

Jeremy Baynes (J)

U.S. Environmental Protection Agency (EPA), Center for Public Health and Environmental Assessment (CPHEA), Environmental Pathways Modeling Branch (EPMB), 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA. Electronic address: Baynes.Jeremy@epa.gov.

Anne Neale (A)

U.S. Environmental Protection Agency (EPA), Center for Public Health and Environmental Assessment (CPHEA), Environmental Pathways Modeling Branch (EPMB), 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.

Classifications MeSH